Model Size Computation. We compare the model size (in terms of number of parameters) of the proposed
A-PixelHop method with that of the other methods in Table REF . Auto-Spec
and the method from [1]} utilized Resnet34 and Resnet50, respectively.
The method from [2]} used a light weight CNN that has two
convolutional layers and one fully connected layer. The model has 1K
parameters. A-PixelHop has different model sizes in Experiments I and
II. As shown in Table REF , it selects 6 and 9
discriminant channels in Experiments I and
II, respectively, and trains one XGBoost classifier
for each channel. Each XGBoost classifer has 100 trees with a maximum
depth of 6 and has a model size of 19K. Furthermore, it
trains an ensemble XGBoost classifier that has 10 trees with depth equal
to one. Its model size is 40.
